Data sets with hundreds of variables arise today in many contexts, including, for example: gene expression data for uncovering the link between the genome and the various proteins for which it codes; demographic and consumer profiling data for capturing underlying sociological and economic trends; sales and marketing data for huge numbers of products in vast and ever-changing marketplaces; and environmental measurements for understanding phenomena such as pollution, meteorological changes and resource impact issues.
Data visualization is a powerful tool for exploring large data sets, both by itself and coupled with data mining algorithms. Graphical views provide user-friendly ways to visualize and interpret data.
However, the task of effectively visualizing large databases imposes significant demands on the human-computer interface to the visualization system. Even specifying what should be calculated and shown in a chart or other data visualization can be problematic.
Consequently, there is a need for faster, more efficient methods and interfaces for selecting and displaying chart calculation options. Such methods and interfaces may complement or replace conventional methods for selecting and displaying chart calculation options. Such methods and interfaces reduce the cognitive burden on a user and produce a more efficient human-machine interface. For battery-operated devices, such methods and interfaces conserve power and increase the time between battery charges.